Nosocomial infections are common in many hospital departments, but particularly so on the intensive care unit, where they affect some 20 to 30% of patients. While early diagnosis and appropriate treatment are, of course, important, perhaps the greatest challenge is in the application of techniques to limit the development of such infections. This review will briefly discuss some of the background pathophysiology and epidemiology of nosocomial infection, and then focus on general and infection-specific preventative strategies individually and as part of broader infection-control programs with infection surveillance.
SCOPUS: ed.j
La Presse Medicale - In Press.Proof corrected by the author Available online since mardi 12 avril 2016
The practice of medicine is predicated on discovering commonalities or distinguishing characteristics among patients to inform corresponding treatment. Given a patient grouping (hereafter referred to as a phenotype), clinicians can implement a treatment pathway accounting for the underlying cause of disease in that phenotype. Traditionally, phenotypes have been discovered by intuition, experience in practice, and advancements in basic science, but these approaches are often heuristic, labor intensive, and can take decades to produce actionable knowledge. Although our understanding of disease has progressed substantially in the past century, there are still important domains in which our phenotypes are murky, such as in behavioral health or in hospital settings. To accelerate phenotype discovery, researchers have used machine learning to find patterns in electronic health records, but have often been thwarted by missing data, sparsity, and data heterogeneity. In this study, we use a flexible framework called Generalized Low Rank Modeling (GLRM) to overcome these barriers and discover phenotypes in two sources of patient data. First, we analyze data from the 2010 Healthcare Cost and Utilization Project National Inpatient Sample (NIS), which contains upwards of 8 million hospitalization records consisting of administrative codes and demographic information. Second, we analyze a small (N=1746), local dataset documenting the clinical progression of autism spectrum disorder patients using granular features from the electronic health record, including text from physician notes. We demonstrate that low rank modeling successfully captures known and putative phenotypes in these vastly different datasets.
info:eu-repo/semantics/published
An elderly patient dies from septic shock in the intensive care unit. This is perhaps not an unusual scenario, but in this case the sepsis happens to have been due to methicillin-resistant Staphylococcus aureus, possibly related to a catheter, and possibly transmitted from a patient in a neighbouring room by less than adequate compliance with infection control procedures. The family decides to sue. We present how experts from four different countries assess the medicolegal issues involved in this case.
